Reliable personal authentication is important for security in a networked society. Many physiological characteristics of humans, such as biometrics, are typically time invariant, easy to acquire, and unique for every individual. Biometric features such as face, iris, fingerprint, hand geometry, palm print, and hand signature have been suggested for the security relating to access control. Most of the current research in biometrics has been focused on fingerprints and the face. The reliability of personal identification using the face is currently low as researchers continue to grapple with the problems of pose, lighting, orientation and gesture. Fingerprint identification is widely used in personal identification as it works well in most cases. However, it is difficult to acquire fingerprint features such as minutiae, for some class of persons such as manual laborers, and elderly people. Several biometrics technologies are such susceptible to spoof attacks in which fake fingerprints, static palm prints, static face and iris images can be successfully employed as biometric samples to impersonate in the process of identification. Consequently, other biometric characteristics are receiving increasing attention.
Finger vein images acquired from near infrared or thermal infrared based optical imaging offers promising alternatives. The hand based biometric modalities which can be acquired in non-contact manner are most attractive in the foreseen market because of hygienic benefits which generates higher user-acceptance.
Personal identification using finger vein patterns have invited a great deal of research interest and currently several commercial products are available for civilian applications.
Finger vein identification using Radon transform based statistical features and a probabilistic neural network classifier has been successful. However the database employed in this work is too small to generate reliable conclusion on the stability of such features in the noisy vein patterns. Curvelet based extraction of finger vein patterns and its classification using back-propagation neural networks have been used. The performance from this approach is shown to be very high but the key details of their implementation are unknown. The restoration of finger vein images using point spread functions has also been investigated. There has been significant improvement in the performance of vein identification using such restored finger images. However, the finger vein imaging setup is rather constrained and restricts the rotation or the movement of fingers during the imaging. Most of the prior art devices require close contact of frontal finger surface with the imaging surface of the devices. There remains problems relating to hygiene, performance and user acceptance of finger vein biometrics devices and systems.